Operations analysis is usually conducted to develop and understand the operational processes of an organization. It uses logical reasoning, statistical analysis, and mathematical models to indicate if all the organization’s areas are contributing to the performance and the advancing of the organization’s strategy. This paper aims to determine the reasons as to why the strategic objective of the organization should be considered by operations analysis while indicating the things to consider during the selection of operational metrics as well as describing quantitative tools that are used to develop benchmarks.
The strategic objective of an organization should be considered in operations analysis because operations analysis itself is a strategy which works to make sure that the organization’s operation plans are in line with the strategic planning. Operations analysis through an examination of the performance of the operations of a particular investment and measuring it against the set of strategic performance goals, or objectives, it can be able to reveal the weaknesses and strengths of the organizations and any of the opportunities available to improve performance ( Nahmias & Cheng, 2005) .
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The objective of operation analysis is to reassess the existing objective, strategic planning, and determining how the goals can be met better hence the importance of operations analysis in considering the strategic aim of an organization.
In choosing the right metrics for the operations analysis, some important things should be considered. The organization should think whether the parameter will be essential and related to the organization. The parameters to be selected should be connected to the strategic objectives and should be accepted by all people in the organization since the settings have to drive the behavior of the employees as desired ( Jiang, 2015) .
The ease and simplicity of interpreting the metrics should also be considered. The metric to be chosen should be easy to understand and interpret for all the people in the organization. The essential information presented by the metrics should be visible and self-evident all the time to ensure exceptions can be acted upon and seen clearly ( Garcea et al., 2006) . Parameters that are complicated to understand and interpret should be avoided at all costs.
The organization should also consider if the metrics are Specific, Measurable, Attainable, Relevant, and Time-bound (SMART) ( Jiang, 2015) . SMART parameters are a good guideline for checking on goals. Therefore, it is necessary for the organization to consider this factor while choosing the right parameters for the operations analysis.
The best quantitative tool that can be used to develop benchmarks for an organization is the six sigma tools. Six Sigma consists of different tools and techniques to improve the processes of an organization. Its strategies are aimed at output process quality improvement through identification and removal of the causes of the defects while minimizing the variability in the organization’s processes ( Harry et al., 2010) . A Six Sigma project usually has particular value targets such as improving performance, increasing profits among others, and it follows specified steps that are sequential, making it an excellent tool for developing benchmarks.
The merits of Six Sigma are its clear focus on ensuring that it achieves a quantifiable and measurable financial return from any of its projects. The fact that it is results-oriented makes it worth using in the benchmarking process since every organization's desire is to increase profits while minimizing costs. It also has the advantage of growing emphasis on passionate and strong management support and leadership a key component of developing benchmarks ( Harry et al., 2010) . Six Sigma is committed to making decisions based on statistical methods and data that can be verified and not making decisions on guesswork and assumptions.
The demerits of the tool are, it is only designed to handle processes already in place and not assist in developing new processes, products, or technologies. It is also very over-reliant on statistical models making it hard to pay attention to robustness development.
References
Garcea, F., Murstein, M. S., Sprague, R. W., Sutton, A. M., Thomas, M. W., & Zizys, G. (2006). U.S. Patent No. 7,111,059 . Washington, DC: U.S. Patent and Trademark Office.
Harry, M. J., Mann, P. S., De Hodgins, O. C., Hulbert, R. L., & Lacke, C. J. (2010). Practitioner's guide to statistics and lean six sigma for process improvements . John Wiley & Sons.
Jiang, Y. (2015). Prioritizing and selecting KPIs: An AHP-based model to evaluate the alignments among strategy business model and performance metrics.
Nahmias, S., & Cheng, Y. (2005). Production and operations analysis (Vol. 6). New York: McGraw-hill.
